Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery

Abstract

Repeated observation of a given area over time yields potential for many forms of change detection analysis. These repeated observations are confounded in terms of radiometric consistency due to changes in sensor calibration over time, differences in illumination, observation angles and variation in atmospheric effects. This paper demonstrates applicability of an empirical relative radiometric normalization method to a set of multitemporal cloudy images acquired by Resourcesat1 LISS III sensor. Objective of this study is to detect and remove cloud cover and normalize an image radiometrically. Cloud detection is achieved by using Average Brightness Threshold (ABT) algorithm. The detected cloud is removed and replaced with data from another images of the same area. After cloud removal, the proposed normalization method is applied to reduce the radiometric influence caused by non surface factors. This process identifies landscape elements whose reflectance values are nearly constant over time, i.e. the subset of non-changing pixels are identified using frequency based correlation technique. The quality of radiometric normalization is statistically assessed by R 2 value and mean square error (MSE) between each pair of analogous band. Keywords—Correlation, Frequency domain, Multitemporal, Relative Radiometric Correction I. INTRODUCTION ATELLITE images are useful for monitoring changes in land use and land cover. But major problem with these images is that regions below clouds are not covered by sensor. The image distortion due to cloud cover is a classical problem of visible band of remote sensing imagery. Especially, for non-stationary satellite, it is commonly found in the earth resource observation application. Removing cloud cover from satellite imagery is very useful for assisting image interpretation. Hence cloud detection and removal is very vital in processing of satellite imagery. Further it is more difficult to quantify and interpret changes on multitemporal images under different illumination, atmospheric or sensor conditions without radiometric calibration. The relative approach to radiometric correction, known as relative radiometric normalization, is preferred because no in situ atmospheric data at the time of satellite overpasses are required (1).